Nothing Has a Right to Exist in Your Portfolio | What the Last 15 Years Has Taught Us

In the latest episode of Click Beta, Matt Zeigler, Dave Nadig and Cameron Dawson take a look back at 2025 and a look forward to 2026. Subscribe to Click Beta via the links below. Follow Click Beta:Spotify⁠https://open.spotify.com/show/0u1fxie4C4vHXIJPUMhvUs⁠Apple Podcasts⁠https://podcasts.apple.com/ky/podcast/click-beta/id1793929457⁠YouTube:⁠https://www.youtube.com/excessreturns

In this episode of Excess Returns, we sit down with Adrian Helfert of Westwood to discuss how investors should be thinking about portfolio construction in a market shaped by artificial intelligence, high levels of concentration, shifting interest rate dynamics, and evolving economic signals. The conversation covers how AI-driven capital spending is changing return profiles across markets, why traditional investing rules are breaking down, and how investors can balance growth, income, and risk in an uncertain environment. Adrian shares his framework for understanding return drivers, his views on market concentration and valuation, and how to think about diversification, macro risk, and income generation going forward.Main topics covered• How Westwood frames portfolio construction around capital appreciation, income, and event-driven returns• Why AI spending is both a major opportunity and a growing existential risk for large companies• The sustainability of market concentration and what it means for future returns• Whether higher interest rates really hurt growth stocks the way investors expect• How massive data center and AI capital expenditures could translate into productivity gains• The case for market broadening beyond the Magnificent Seven• Why traditional recession indicators have failed in recent cycles• How inflation, labor markets, and Federal Reserve policy interact today• Rethinking the classic 60/40 portfolio and the role of private markets• Using covered calls and active income strategies to manage risk and generate yieldTimestamps00:00 Introduction and near-term opportunities versus long-term risk02:40 Capital appreciation, income, and event-driven investing framework06:30 Have markets structurally changed to support higher returns09:30 Intangible assets, AI, and margin expansion10:20 The scale of AI and data center capital spending13:00 Productivity gains and return on investment from AI16:00 AI as both opportunity and risk for companies19:30 Market concentration and diversification concerns23:30 Will market leadership eventually broaden25:30 Growth stocks, duration, and interest rates29:30 International diversification and global investing33:30 Why recession indicators have failed39:00 Inflation outlook and Federal Reserve policy46:00 Rethinking the 60/40 portfolio53:00 Enhanced income strategies and covered calls59:00 One investing belief most peers disagree with

In this special episode, Adam Butler and Ben Hunt join Matt Zeigler to unpack one of the most charged debates in markets and economics today: whether our official statistics still reflect lived reality. Building on Mike Green’s work and Adam Butler’s essay The Bureau of Missing Children, the conversation moves beyond the technical definition of poverty to a deeper idea of economic precarity, the growing gap between what we measure and what people actually experience. Together, they explore debt, housing, childcare, labor mobility, AI, and the erosion of meaning in economic language, while wrestling with what policy, community, and human-centered solutions might look like in a world that increasingly feels unstable.Main topics coveredWhy the debate should focus on precarity rather than povertyThe disconnect between inflation statistics and lived experienceHow debt, housing, childcare, and education drive economic insecurityThe idea of a participation budget for modern family formationWhy labor mobility has broken down since the financial crisisHow asset prices and credit intensify risk for householdsThe role of grandparents and off-balance-sheet support in the economyDarwin’s wedge, positional goods, and rising costs of everyday lifeThe impact of AI, technocracy, and anti-human incentivesCentralized versus decentralized solutions to today’s economic challengesWhat it means to carry the fire and preserve human-centered valuesTimestamps00:00 Introduction and the emotional roots of the precarity debate02:00 Poverty versus precarity and what we are really measuring06:30 Technocrats, narratives, and the limits of economic statistics09:00 Personal experiences with precarity and debt15:00 The Bureau of Missing Children and family formation economics21:00 Modeling household income and participation budgets25:50 Rising costs of childcare, housing, and everyday life33:00 Darwin’s wedge and positional competition36:45 Debt, housing, and labor immobility40:00 Grandparents, unpaid care, and off-balance-sheet subsidies46:30 How today differs from 40 or 50 years ago49:40 Labor mobility as a lost engine of opportunity55:00 Policy paths, mission-driven economics, and decentralization01:11:00 Visionary leadership versus bottom-up solutions01:15:50 Carrying the fire and preserving meaning01:17:30 Where to follow Adam Butler and Ben Hunt

In this episode of Excess Returns, we sit down with David Wright, Head of Quantitative Investing at Pictet Asset Management, for a deep and practical conversation about how artificial intelligence and machine learning are actually being used in real-world investment strategies. Rather than focusing on hype or black-box promises, David walks through how systematic investors combine human judgment, economic intuition, and machine learning models to forecast stock returns, construct portfolios, and manage risk. The discussion covers what AI can and cannot do in investing today, how machine learning differs from traditional factor models and large language models like ChatGPT, and why interpretability and robustness still matter. This episode is a must-watch for investors interested in quantitative investing, AI-driven ETFs, and the future of systematic portfolio construction.Main topics covered:What artificial intelligence and machine learning really mean in an investing contextHow machine learning models are trained to forecast relative stock returnsThe role of features, signals, and decision trees in quantitative investingKey differences between machine learning models and large language models like ChatGPTWhy interpretability and stability matter more than hype in AI investingHow human judgment and machine learning complement each other in portfolio managementData selection, feature engineering, and the trade-offs between traditional and alternative dataOverfitting, data mining concerns, and how professional investors build guardrailsTime horizons, rebalancing frequency, and transaction cost considerationsHow AI-driven strategies are implemented in diversified portfolios and ETFsThe future of AI in investing and what it means for investorsTimestamps:00:00 Introduction and overview of AI and machine learning in investing03:00 Defining artificial intelligence vs machine learning in finance05:00 How machine learning models are trained using financial data07:00 Machine learning vs ChatGPT and large language models for stock selection09:45 Decision trees and how machine learning makes forecasts12:00 Choosing data inputs: traditional data vs alternative data14:40 The role of economic intuition and explainability in quant models18:00 Time horizons and why machine learning works better at shorter horizons22:00 Can machine learning improve traditional factor investing24:00 Data mining, overfitting, and model robustness26:00 What humans do better than AI and where machines excel30:00 Feature importance, conditioning effects, and model structure32:00 Model retraining, stability, and long-term persistence36:00 The future of automation and human oversight in investing40:00 Why ChatGPT-style models struggle with portfolio construction45:00 Portfolio construction, diversification, and ETF implementation51:00 Rebalancing, transaction costs, and practical execution56:00 Surprising insights from machine learning models59:00 Closing lessons on investing and avoiding overtrading

In this episode of our new show The 100 Year Thinkers, Robert Hagstrom, Chris Mayer, Bogumil Baranowki and Matt Zeigler explain how investors get trapped by labels, abstractions, and simplistic models, and why breaking free with better mental models, language, and long-term thinking is a real edge in markets.Subscribe on Spotify⁠⁠https://open.spotify.com/show/5IsVVM27KWP6SUW6KN2ife⁠⁠Subscribe on Apple Podcasts⁠⁠https://podcasts.apple.com/us/podcast/the-100-year-thinkers-long-term-compounding-in-a-short-term-world/id1845466003⁠⁠Subscribe on YouTube⁠⁠https://youtube.com/@excessreturns⁠

Brent Kochuba takes a look behind the scenes at the options flows driving the market heading into the December options expiration and the end of 2025. Subscribe on Spotify⁠https://open.spotify.com/show/4KR2YVJqk2lnVETMKDavJf⁠Subscribe on Apple Podcasts⁠https://podcasts.apple.com/us/podcast/the-opex-effect/id1711880009⁠Subscribe on YouTube⁠https://www.youtube.com/channel/UCPYvx_y92dvI1PSdiho0ALw

Ed Yardeni returns to Excess Returns to break down the evolving market landscape, why he moved the Magnificent 7 to underweight, and how AI, productivity, interest rates, global markets, and sector leadership will shape the next stage of the Roaring 2020s. Ed explains why the economy has remained so resilient, what could finally trigger a true market broadening, and how investors should think about everything from tech competition to inflation, private credit risks, and Fed policy heading into 2026.Main topics covered• Why Ed reduced the Magnificent 7 and tech from overweight to market weight• How extreme sector concentration affects portfolio construction• The escalating competition inside AI and large-cap tech• The AI CapEx boom and how it changes earnings, margins, and valuation• Valuation considerations for tech leaders at this stage of the cycle• Whether the Mag 7 should be compared to past tech bubbles• How AI adoption may spread to the broader economy and boost productivity• Economic impact of AI on jobs, wages, and long-term inflation• Why the US economy avoided recession despite persistent warnings• Rolling recessions vs traditional recessions and how they shape markets• Private credit risks and whether they pose a systemic threat• Prospects for small caps, mid caps, financials, industrials, and healthcare• Why 2026 may finally bring true market broadening• The outlook for international investing and emerging markets• Ed’s S&P 500 roadmap to 7,700 next year and 10,000 by 2029• Fed policy, rate cuts, inflation, bond vigilantes, and political pressure• Key risks investors should monitor heading into 2026Timestamps00:00 Mag 7 concentration and the case for rebalancing03:00 How Ed builds probability-based market scenarios04:30 Why the Roaring 2020s thesis still holds06:00 The no-show recession and economic resilience07:00 Why he moved the Mag 7 and tech to market weight09:30 How every company is becoming a technology company12:20 Knowing when a successful thesis has run its course13:30 The dominance of the US market and global diversification15:00 Why market weight, not overweight, for tech and the Mag 716:00 Tech competition, AI leapfrogging, and margin pressure18:30 The CapEx boom and valuation questions21:00 Comparing today’s tech leaders to the 2000 era23:00 How AI could lift productivity across the entire economy25:00 Putting AI in historical context27:00 How new technologies solve constraints like energy and compute29:00 AI’s long-term impact on productivity and growth30:00 Labor market disruption and job transition dynamics31:20 Will AI be deflationary over time?32:30 Technology, China, automation, and global deflation forces33:00 Ed’s forecast for the S&P 500 through 202935:00 Why recession indicators failed this cycle37:00 How liquidity facilities prevent credit crunches39:00 Private credit risks and transparency challenges40:45 The potential for market broadening in 202642:20 Takeaways from the latest Fed meeting44:00 Should the Fed be cutting rates?45:00 Fed independence under political pressure47:00 Why bond vigilantes may return in 202648:00 International investing opportunities and ETFs49:30 Closing thoughts and key risks ahead

In this episode of Excess Returns, we sit down with Andrew Beer to break down managed futures, hedge fund replication, diversification, and what investors can realistically expect from these alternative strategies. Andrew explains why managed futures can act like a “cloudy crystal ball,” how trend strategies capture major macro shifts, why complexity isn’t always your friend, and how advisors can communicate these concepts to clients. We also explore fees, model portfolios, allocation decisions, global macro themes, and what smart-money positioning looks like heading into 2025.Topics CoveredWhat managed futures actually are and how they workHow trend strategies capture big macro shiftsWhy diversification is most valuable during market stressWhy investors struggle with complexity and line-item riskThe statistical case for adding managed futures to a 60/40 portfolioBarriers to adoption and how advisors should explain the strategyThe role of model portfolios and why slow rebalancing can hurt in regime shiftsWhy Andrew prefers simplicity over complexity in managed futuresFee sensitivity, ETFs, and how this strategy goes mainstreamIndexing, replication, and building more efficient alternativesWhy manager selection is hard in this spaceThe “rush to complexity” and why it often hurts returnsHow hedge fund replication works and what it capturesWhat smart money is positioned for today across equities, rates, currencies, and commoditiesMacro themes: inflation, rate cycles, the dollar, yen, and global equity opportunitiesWhy international equities may finally be turningHow managed futures complement – not replace – stocks and bondsWhat mainstream adoption might look like over the next decadeTimestamps00:00 Intro and why managed futures matter02:00 Explaining managed futures in simple terms06:18 The four major asset classes trend funds trade10:00 Why trends form and how information reveals itself in prices11:55 Diversification and how managed futures improve portfolios14:00 Why investors haven’t widely adopted the strategy17:01 Communicating the “what,” not the “how,” with clients18:55 How model portfolios behave in regime change21:55 How managed futures can move faster than traditional allocations24:00 Why a simple portfolio of major markets works26:00 Making alternatives feel less risky28:00 Performance dispersion across managed futures ETFs30:00 Why complexity doesn’t equal value35:20 Fees, ETFs, and what mainstream adoption requires38:00 The real reason for the industry’s “rush to complexity”40:35 Should managed futures exclude equities and bonds?43:00 Why it’s so hard to handicap what will work in advance46:00 The human side of alternatives and advisor communication47:00 Hedge fund replication explained50:00 How replication identifies major themes52:00 Why replication works only in certain strategies53:10 What smart money positioning looks like today55:45 Inflation, rates, the dollar, and global opportunities58:00 The path to managed futures becoming a standard allocation59:22 Where to find Andrew Beer online

We are including this episode from our separate show Teach Me Like I'm Five in the Excess Returns feed. If you would like to continue receiving new episodes, subscribe using the links below.In the episode, we sit down with Business Breakdowns host Matt Reustle to discuss how he breaks down businesses and the common characteristics that the best businesses he has looked at share. Subscribe on Spotifyhttps://open.spotify.com/show/7zu6lFpPohoPKhcu0Er9kBSubscribe on Apple Podcastshttps://podcasts.apple.com/hr/podcast/teach-me-like-im-five-investing-concepts-made-simple/id1815975642

In this episode of Excess Returns, Graeme Forster of Orbis joins us to discuss two major research papers: Six Courageous Questions for 2026 and Sunrise on Venus. We explore how long-running global trends may be reversing, what that means for U.S. dominance, the future of international and emerging markets, the risks and opportunities created by AI and massive CapEx spending, the dollar’s shifting role, and how investors should think about valuation, humility, and navigating a world where the economic “water” is changing. This conversation is packed with global macro insight, long-term investing lessons, and practical frameworks for building more resilient portfolios. Topics Covered:• Why long-term market “water” becomes invisible to investors• Self-reinforcing global cycles and how China’s WTO entry reshaped the world• Signs the 25-year U.S. outperformance cycle may be breaking• How tariffs, political shifts, and corporate reforms change the global landscape• Why international and emerging markets may now offer better expected returns• Why U.S. large caps are not the entire story of American exceptionalism• How to think about valuation, margins, and discounted cash flow models across markets• The AI boom, bubbles, capital cycles, and asymmetric outcomes• How AI CapEx constraints influence winners and losers• The shifting role of the U.S. dollar and why market shocks may behave differently• Maslow’s hierarchy, needs vs. wants, and the return of state-driven capital investment• Deglobalization, reshoring, and the national-security lens for investing• How to evaluate China and Taiwan inside emerging markets• Why humility is an investor’s greatest edgeTimestamps:00:00 Introduction01:02 Why Orbis wrote Six Courageous Questions for 202603:44 The David Foster Wallace “water” analogy and investing06:12 How a 25-year self-reinforcing cycle powered U.S. outperformance10:12 Signs the cycle may be breaking12:00 Corporate reform and opportunity in Asia13:55 Why active share, benchmarking, and incentives distort investor behavior17:31 Decomposing S&P 500 returns: margins, valuations, fundamentals20:20 Expected returns inside and outside the U.S.22:34 Why international stocks offer richer opportunity sets24:25 Currency implications and weakening dollar dynamics26:18 American exceptionalism beyond the top 10 mega caps28:49 Where Orbis is finding value today30:25 Biotech, healthcare, and post-COVID dislocation31:05 How Orbis thinks about valuation in an intangible-heavy world32:09 Is AI a bubble or the beginning of something bigger?34:30 Game theory of AI CapEx and right-tail outcomes36:00 CapEx cycles, history, and who benefits38:00 Indirect AI beneficiaries and the SK Square example40:35 Maslow’s hierarchy and the shift from wants to needs42:32 Deglobalization, national security, and domestic reinvestment44:00 Capital returning to home markets and strategic industries46:00 Can anything reverse these structural trends?48:00 Balancing bottom-up investing with macro awareness49:45 The deeper risk in emerging markets: owning vs. avoiding51:00 Valuation still matters for long-term returns52:29 Corporate behavior, dividends, and re-rating cycles53:52 How Orbis views China vs. bottom-up opportunity55:34 Why great investors must be right 90–95% of the time in decision quality58:00 One lesson Graeme would teach the average investor